This repository provides containerized versions of hundreds of Model Context Protocol (MCP) servers, simplifying the setup and execution of various AI-powered tools and services. It targets developers and AI practitioners looking to easily integrate external APIs and data sources into their LLM workflows, offering a vast ecosystem of pre-built integrations.

How It Works

The project leverages Nixpacks to automatically build Docker images for numerous MCP servers. These servers act as bridges, translating natural language requests from LLM clients (like Claude Desktop or Cursor) into API calls for specific services. This approach abstracts away the complexity of individual API integrations, providing a standardized interface for AI agents to access diverse functionalities.

Quick Start & Requirements

• Install/Run: Pull the desired Docker image for a specific MCP server (e.g., docker pull metorial/mcp-github).
• Prerequisites: Docker.
• Setup: Minimal setup time, primarily involving Docker installation and pulling images.
• Resources: Refer to individual MCP server documentation for specific resource requirements.

Highlighted Details

• Vast collection of MCP servers for diverse services (APIs, databases, cloud platforms, etc.).
• Daily automatic updates ensure containers are always current.
• Nixpacks used for automated container builds.
• Community-driven contribution model for adding new MCP servers.

Maintenance & Community

• Actively maintained by Metorial.
• Contributions are welcomed via issues and pull requests.
• Contributing Guide available for new server integrations.

Licensing & Compatibility

• License: MIT License for the repository's code. Container source code adheres to their respective project licenses.
• Compatibility: Generally compatible with any MCP-compatible client. Commercial use is subject to the licenses of individual MCP server integrations.

Limitations & Caveats

The README lists hundreds of MCP servers, but specific server functionality, performance, and maintenance status may vary. Users should consult the documentation for each individual MCP server for detailed information and potential limitations.